Instructions to use Sathvik0101/cyber-duel-tiny-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sathvik0101/cyber-duel-tiny-adapter with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("google/gemma-3-270m-it")
model = PeftModel.from_pretrained(base_model, "Sathvik0101/cyber-duel-tiny-adapter")

Transformers

How to use Sathvik0101/cyber-duel-tiny-adapter with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Sathvik0101/cyber-duel-tiny-adapter")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Sathvik0101/cyber-duel-tiny-adapter", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Sathvik0101/cyber-duel-tiny-adapter with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sathvik0101/cyber-duel-tiny-adapter"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sathvik0101/cyber-duel-tiny-adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Sathvik0101/cyber-duel-tiny-adapter

SGLang

How to use Sathvik0101/cyber-duel-tiny-adapter with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Sathvik0101/cyber-duel-tiny-adapter" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sathvik0101/cyber-duel-tiny-adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Sathvik0101/cyber-duel-tiny-adapter" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sathvik0101/cyber-duel-tiny-adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Sathvik0101/cyber-duel-tiny-adapter with Docker Model Runner:
```
docker model run hf.co/Sathvik0101/cyber-duel-tiny-adapter
```

cyber-duel-tiny-adapter / ref /adapter_model.safetensors

Sathvik0101

Upload cyber-duel-tiny LoRA adapter (SFT)

7651221 verified 15 days ago

Download

History Blame Contribute Delete

15.2 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: f9a5660e2392ad05b4c14eb440b93005a8994837e18db5e856138f87df140a8b
Size of remote file:: 15.2 MB
SHA256:: 1435aaec17f56e3fd3d82fd66514ac8b1261af720fe0159a80b04fa37617ee55

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.